Diffusion-weighted (DWI) and perfusion-weighted (PWI) magnetic resonance (MR) imaging have been shown to be highly sensitive and specific in diagnosing acute human cerebral ischemia. These imaging techniques appear to provide superior early identification of regions likely to proceed to infarction, compared to conventional MR or CT imaging. However, the prediction of tissue and clinical outcome from specific imaging characteristics remains challenging. Although studies have found correlations between acute DWI and PWI with patients' clinical and follow-up imaging outcomes, the ability to predict clinical or tissue outcome in individual patients using a single modality is limited using conventional techniques.
Attempts have been made to combine DWI and PWI by comparing lesion volumes identified by the two techniques. “Diffusion-perfusion mismatches,” in which the lesion volumes identified by one modality are larger than those by the other, have been reported by several groups. Some groups have reported larger lesion enlargement of the acute DWI lesion volume in cases where the acute PWI volume is larger than the DWI lesion. In cases where the acute DWI lesion was larger than the PWI lesion, total lesion growth was reduced.
However, these reported “mismatches” are of volumes of tissue rather than a voxel-by-voxel comparison. Heterogeneity in both apparent diffusion coefficient (ADC) and flow values within acute ischemic tissue in humans have been well documented but have not been captured in these initial volumetric approaches. Therefore, volumetric approaches comparing gross differences in DWI and PWI lesion volumes may oversimplify the complex task of assessing tissue viability in different regions within ischemic tissue.
One known voxel-by-voxel was developed by Welch et al., “A Model to Predict the Histopathology of Human Stroke Using Diffusion and T2-Weighted Magnetic Resonance Imaging,” Stroke. 1995;26:1983-1989. Welch's approach provide a more sensitive approach for identifying salvageable tissue by demonstrating that a combination of T2 and ADC information provided better prediction of cellular necrosis than algorithms that used them separately and that a voxel-by-voxel analysis may better demonstrate the underlying heterogeneity in the lesion. These studies implemented their predictive algorithms using thresholding techniques in which tissue is classified as abnormal if a measured value, e.g., the apparent diffusion coefficient (ADC) or T2WI value, is 1.5-2 standard deviations away from its mean value in the contralateral hemisphere. Readily assessing the signatures' significance can therefore be complicated as the number of input parameters is increased (d inputs result in 2d states). Another potential problem with a thresholding algorithm is that it ignores the variances intrinsic in the input data. A more appropriate model may be one in which the inputs are considered random variables and the output a probability variable.
It would, therefore, be desirable to provide a voxel-by-voxel risk map indicating the probabilities that tissue will infarct. It would further be desirable to utilize the risk map to evaluate novel interventions.